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- research-articleMay 2023
Towards Sample-Efficient Multi-Objective Reinforcement Learning
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent SystemsMay 2023, Pages 2970–2972In sequential decision-making problems, the objective that a reinforcement learning agent seeks to optimize is often modeled via a reward function. However, in real-world problems, agents often have to optimize multiple (possibly conflicting) objectives. ...
- research-articleMay 2023
Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent SystemsMay 2023, Pages 2961–2963It is challenging to quantify numerical preferences for different objectives in a multi-objective decision-making problem. However, the demonstrations of a user are often accessible. We propose an algorithm to infer linear preference weights from either ...
- research-articleMay 2023
Welfare and Fairness in Multi-objective Reinforcement Learning
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent SystemsMay 2023, Pages 1991–1999We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward. Motivated by the fair resource allocation literature, we model this as ...
- research-articleOctober 2022
Maximum Norm Minimization: A Single-Policy Multi-Objective Reinforcement Learning to Expansion of the Pareto Front
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementOctober 2022, Pages 1064–1073https://doi.org/10.1145/3511808.3557389In this paper, we propose Maximum Norm Minimization (MNM), a single-policy Multi-Objective Reinforcement Learning (MORL) algorithm to solve the multi-objective RL problem. The main objective of our MNM is to provide the Pareto optimal points ...
- research-articleMay 2022
Multi-Objective Reinforcement Learning with Non-Linear Scalarization
AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent SystemsMay 2022, Pages 9–17Multi-Objective Reinforcement Learning (MORL) setup naturally arises in many places where an agent optimizes multiple objectives. We consider the problem of MORL where multiple objectives are combined using a non-linear scalarization. We combine the ...
- research-articleFebruary 2022
Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningFebruary 2022, Pages 316–324https://doi.org/10.1145/3488560.3498487The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems (RS) touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly ...
- research-articleFebruary 2022
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningFebruary 2022, Pages 957–965https://doi.org/10.1145/3488560.3498471Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one pays a significant price in ...
- research-articleOctober 2021
A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds
Frontiers of Computer Science: Selected Publications from Chinese Universities (FCS), Volume 15, Issue 5Oct 2021https://doi.org/10.1007/s11704-020-9273-zAbstractRecently, a growing number of scientific applications have been migrated into the cloud. To deal with the problems brought by clouds, more and more researchers start to consider multiple optimization goals in workflow scheduling. However, the ...
- research-articleSeptember 2021
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems Using Multi-objective Reinforcement Learning
ACM Transactions on Cyber-Physical Systems (TCPS), Volume 5, Issue 4Article No.: 34, Pages 1–24https://doi.org/10.1145/3447623As a new generation of Public Bicycle-sharing Systems (PBS), the Dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use artificial intelligence to provide efficient bicycle dispatching ...
- research-articleMay 2021
Multi-Agent Reinforcement Learning with Temporal Logic Specifications
AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent SystemsMay 2021, Pages 583–592In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour. From a learning perspective these specifications provide a rich formal ...